Support vector machine (SVM) - part 2 / Mathematical formulation of SVM / KTU Machine learning
#svm #supportvectormachine #machinelearning
This lecture decribes support vector machine or svm and mathematical explanation of svm in detail. In a linearly separable two class dataset, we can find many hyperplanes which separates the two classes of data. But the svm problem is to find the best (optimum) separating hyperplane which is having maximum margin. The double of the smallest perpendicular distance from the training instances to separating hyperplane is called margin of that hyperplane. The data points that lie closest to the optimal separating hyperplane are called support vectors.
Notes:
https://drive.google.com/file/d/1EVA77H8w4ytKJyRKVC-yFuRk0VzaHm_z/view?usp=drivesdk
Видео Support vector machine (SVM) - part 2 / Mathematical formulation of SVM / KTU Machine learning канала EduFlair KTU CS
This lecture decribes support vector machine or svm and mathematical explanation of svm in detail. In a linearly separable two class dataset, we can find many hyperplanes which separates the two classes of data. But the svm problem is to find the best (optimum) separating hyperplane which is having maximum margin. The double of the smallest perpendicular distance from the training instances to separating hyperplane is called margin of that hyperplane. The data points that lie closest to the optimal separating hyperplane are called support vectors.
Notes:
https://drive.google.com/file/d/1EVA77H8w4ytKJyRKVC-yFuRk0VzaHm_z/view?usp=drivesdk
Видео Support vector machine (SVM) - part 2 / Mathematical formulation of SVM / KTU Machine learning канала EduFlair KTU CS
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4 января 2021 г. 23:30:04
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